Embodiments of the invention relate to incrementally accumulating context as new observations present themselves, using information co-located, in support of real-time decisioning.
Observations have features, and overlapping features across observations enable context to accrue. Determining how a new observation locates historical context is accomplished by using the new observation's features to locate candidate observations. Since historical observations end up with several needed access paths for future discovery, traditional systems provide several indexes. Implementing several indexes requires physically choosing which index to optimize around, while the other indexes become less optimal access methods (when dealing with large data sets).
Some systems use sharding, which is a technique that uses hash values to evenly distribute data and/or indices across multiple tables. The even distribution of values enables near linear-scale context accumulation in grid computer environments.